« Information excellence leads your business to success ». This slogan, used by many vendors in the world of computing, effectively hides the need for permanent control over the data, their quality and the management rules that govern them.

One of my colleagues recently told me of an experience that has probably already happened to you : the receipt of an invoice (and a credit note, to boot!) for the amount of € 0.00. It happened recently to me with an Internet service provider of whom I was no longer a customer... But let's get back to this famous invoice :

Less poor data is essential

Data quality is about making sure that data are accurate and coherent.

What type of organization, big or small, can say that they're not concerned by data quality ? None, of course.

Many different types of data are collected (for research, transactions, customer or administrative care, demographics, social media activity, surveys, basically all informations aimed at statistics...).

An introduction to data quality

According to a survey from InformationWeek Analytics*, data quality in 2015 is «still the No. 1 "barrier to success" cited by both Business Intelligence and analytics types and information management professionals».

Analogy between a whisky distillery and data management

The similitudes don't typically jump out, do they? But you'll quickly see the point.

On one side, in a whisky distillery we have engineers who build the tubings and alembics and such (the containers), and biologists who are in charge of taking care of the many processes leading to the final product (the content). It's all about measuring the quality and tasting.

Once bottled, the blend of whisky goes to meet the world (and be enjoyed by happy consumers).

Our cavemen ancestors were all about foraging. Times were tough and to survive one needed to go hunt or search for plants. We've come a long way since then of course, but a small part of us still reacts pretty much the same way. We consider accumulation as a richess. But what is true for more traditional assets such as gold is not necessarily true for modern business assets such as data.

In a standard Business Intelligence environment, the archetypal journey of data can be summarised as follows: collected from sources, extracted, transformed, loaded into an Enterprise Data Warehouse. accessed and used for reporting across the organisation. But what about valuation ?